Foundations of Statistical Natural Language Processing

By: Christopher Manning, Hinrich Schutze

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Hardcover | 30 July 1999

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Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

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"Statistical natural-language processing is, in my estimation, one ofthe most fast-moving and exciting areas of computer science thesedays. Anyone who wants to learn this field would be well advised toget this book. For that matter, the same goes for anyone who isalready in the field. I know that it is going to be one of the mostwell-thumbed books on my bookshelf." Eugene Charniak , Department of Computer Science, Brown University

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Non-Fiction Language & Linguistics Linguistics Computational Linguistics Computing & I.T.Computer Science Artificial Intelligence Natural Language & Machine Translation Psychology Psychological Theory, Systems, Schools of Thought & Viewpoints Cognitivism, Cognitive Theory

List of Tables	p. xv
List of Figures	p. xxi
Table of Notations	p. xxv
Preface	p. xxix
Road Map	p. xxxv
Preliminaries	p. 1
Introduction	p. 3
Ratinalist and Empiricist Approaches to Language	p. 4
Scientific Content	p. 7
Questions that linguistics should answer	p. 8
Non-categorical phenomena in language	p. 11
Language and cognition as probabilistic phenomena	p. 15
The Ambiguity of Language: Why NLP Is Difficult	p. 17
Dirty Hands	p. 19
Lexical resources	p. 19
Word counts	p. 20
Zipf's laws	p. 23
Collocations	p. 29
Concordances	p. 31
Further Reading	p. 34
Exercises	p. 35
Mathematical Foundations	p. 39
Elementary Probability Theory	p. 40
Probability spaces	p. 40
Conditional probability and independence	p. 42
Bayes' theorem	p. 43
Random variables	p. 45
Expectation and variance	p. 46
Notation	p. 47
Joint and conditional distributions	p. 48
Determining P	p. 48
Standard distributions	p. 50
Bayesian statistics	p. 54
Exercises	p. 59
Essential Information Theory	p. 60
Entropy	p. 61
Joint entropy and conditional entropy	p. 63
Mutual information	p. 66
The noisy channel model	p. 68
Relative entropy or Kullback-Leibler divergence	p. 72
The relation to language: Cross entropy	p. 73
The entropy of English	p. 76
Perplexity	p. 78
Exercises	p. 78
Further Reading	p. 79
Linguistic Essentials	p. 81
Parts of Speech and Morphology	p. 81
Nouns and pronouns	p. 83
Words that accompany nouns: Determiners and adjectives	p. 87
Verbs	p. 88
Other parts of speech	p. 91
Phrase Structure	p. 93
Phrase structure grammars	p. 96
Dependency: Arguments and adjuncts	p. 101
X' theory	p. 106
Phrase structure ambiguity	p. 107
Semantics and Pragmatics	p. 109
Other Areas	p. 112
Further Reading	p. 113
Exercises	p. 114
Corpus-Based Work	p. 117
Getting Set Up	p. 118
Computers	p. 118
Corpora	p. 118
Software	p. 120
Looking at Text	p. 123
Low-level formatting issues	p. 123
Tokenization: What is a word?	p. 124
Morphology	p. 131
Sentences	p. 134
Marked-up Data	p. 136
Markup schemes	p. 137
Grammatical tagging	p. 139
Further Reading	p. 145
Exercises	p. 147
Words	p. 149
Collocations	p. 151
Frequency	p. 153
Mean and Variance	p. 157
Hypothesis Testing	p. 162
The t test	p. 163
Hypothesis testing of differences	p. 166
Pearson's chi-square test	p. 169
Likelihood ratios	p. 172
Mutual Information	p. 178
The Notion of Collocation	p. 183
Further Reading	p. 187
Statistical Inference: n-gram Models over Sparse Data	p. 191
Bins: Forming Equivalence Classes	p. 192
Reliability vs. discrimination	p. 192
n-gram models	p. 192
Building n-gram models	p. 195
Statistical Estimators	p. 196
Maximum Likelihood Estimation (MLE)	p. 197
Laplace's law, Lidstone's law and the Jeffreys-Perks law	p. 202
Held out estimation	p. 205
Cross-validation (deleted estimation)	p. 210
Good-Turing estimation	p. 212
Briefly noted	p. 216
Combining Estimators	p. 217
Simple linear interpolation	p. 218
Katz's backing-off	p. 219
General linear interpolation	p. 220
Briefly noted	p. 222
Language models for Austen	p. 223
Conclusions	p. 224
Further Reading	p. 225
Exercises	p. 225
Word Sense Disambiguation	p. 229
Methodological Preliminaries	p. 232
Supervised and unsupervised learning	p. 232
Pseudowords	p. 233
Upper and lower bounds on performance	p. 233
Supervised Disambiguation	p. 235
Bayesian classification	p. 235
An information-theoretic approach	p. 239
Dictionary-Based Disambiguation	p. 241
Disambiguation based on sense definitions	p. 242
Thesaurus-based disambiguation	p. 244
Disambiguation based on translations in a second-language corpus	p. 247
One sense per discourse, one sense per collocation	p. 249
Unsupervised Disambiguation	p. 252
What Is a Word Sense?	p. 256
Further Reading	p. 260
Exercises	p. 262
Lexical Acquisition	p. 265
Evaluation Measures	p. 267
Verb Subcategorization	p. 271
Attachment Ambiguity	p. 278
Hindle and Rooth (1993)	p. 280
General remarks on PP attachment	p. 284
Selectional Preferences	p. 288
Semantic Similarity	p. 294
Vector space measures	p. 296
Probabilistic measures	p. 303
The Role of Lexical Acquisition in Statistical NLP	p. 308
Further Reading	p. 312
Grammar	p. 315
Markov Models	p. 317
Markov Models	p. 318
Hidden Markov Models	p. 320
Why use HMMs?	p. 322
General form of an HMM	p. 324
The Three Fundamental Questions for HMMs	p. 325
Finding the probability of an observation	p. 326
Finding the best state sequence	p. 331
The third problem: Parameter estimation	p. 333
HMMs: Implementation, Properties, and Variants	p. 336
Implementation	p. 336
Variants	p. 337
Multiple input observations	p. 338
Initialization of parameter values	p. 339
Further Reading	p. 339
Part-of-Speech Tagging	p. 341
The Information Sources in Tagging	p. 343
Markov Model Taggers	p. 345
The probabilistic model	p. 345
The Viterbi algorithm	p. 349
Variations	p. 351
Hidden Markov Model Taggers	p. 356
Applying HMMs to POS tagging	p. 357
The effect of initialization on HMM training	p. 359
Transformation-Based Learning of Tags	p. 361
Transformations	p. 362
The learning algorithm	p. 364
Relation to other models	p. 365
Automata	p. 367
Summary	p. 369
Other Methods, Other Languages	p. 370
Other approaches to tagging	p. 370
Languages other than English	p. 371
Tagging Accuracy and Uses of Taggers	p. 371
Tagging accuracy	p. 371
Applications of tagging	p. 374
Further Reading	p. 377
Exercises	p. 379
Probabilistic Context Free Grammars	p. 381
Some Features of PCFGs	p. 386
Questions for PCFGs	p. 388
The Probability of a String	p. 392
Using inside probabilities	p. 392
Using outside probabilities	p. 394
Finding the most likely parse for a sentence	p. 396
Training a PCFG	p. 398
Problems with the Inside-Outside Algorithm	p. 401
Further Reading	p. 402
Exercises	p. 404
Probabilistic Parsing	p. 407
Some Concepts	p. 408
Parsing for disambiguation	p. 408
Treebanks	p. 412
Parsing models vs. language models	p. 414
Weakening the independence assumptions of PCFGs	p. 416
Tree probabilities and derivational probabilities	p. 421
There's more than one way to do it	p. 423
Phrase structure grammars and dependency grammars	p. 428
Evaluation	p. 431
Equivalent models	p. 437
Building parsers: Search methods	p. 439
Use of the geometric mean	p. 442
Some Approaches	p. 443
Non-lexicalized treebank grammars	p. 443
Lexicalized models using derivational histories	p. 448
Dependency-based models	p. 451
Discussion	p. 454
Further Reading	p. 456
Exercises	p. 458
Applications and Techniques	p. 461
Statistical Alignment and Machine Translation	p. 463
Text Alignment	p. 466
Aligning sentences and paragraphs	p. 467
Length-based methods	p. 471
Offset alignment by signal processing techniques	p. 475
Lexical methods of sentence alignment	p. 478
Summary	p. 484
Exercises	p. 484
Word Alignment	p. 484
Statistical Machine Translation	p. 486
Further Reading	p. 492
Clustering	p. 495
Hierarchical Clustering	p. 500
Single-link and complete-link clustering	p. 503
Group-average agglomerative clustering	p. 507
An application: Improving a language model	p. 509
Top-down clustering	p. 512
Non-Hierarchical Clustering	p. 514
K-means	p. 515
The EM algorithm	p. 518
Further Reading	p. 527
Exercises	p. 528
Topics in Information Retrieval	p. 529
Some Background on Information Retrieval	p. 530
Common design features of IR systems	p. 532
Evaluation measures	p. 534
The probability ranking principle (PRP)	p. 538
The Vector Space Model	p. 539
Vector similarity	p. 540
Term weighting	p. 541
Term Distribution Models	p. 544
The Poisson distribution	p. 545
The two-Poisson model	p. 548
The K mixture	p. 549
Inverse document frequency	p. 551
Residual inverse document frequency	p. 553
Usage of term distribution models	p. 554
Latent Semantic Indexing	p. 554
Least-squares methods	p. 557
Singular Value Decomposition	p. 558
Latent Semantic Indexing in IR	p. 564
Discourse Segmentation	p. 566
TextTiling	p. 567
Further Reading	p. 570
Exercises	p. 573
Text Categorization	p. 575
Decision Trees	p. 578
Maximum Entropy Modeling	p. 589
Generalized iterative scaling	p. 591
Application to text categorization	p. 594
Perceptrons	p. 597
k Nearest Neighbor Classification	p. 604
Further Reading	p. 607
Tiny Statistical Tables	p. 609
Bibliography	p. 611
Index	p. 657
Table of Contents provided by Syndetics. All Rights Reserved.

Foundations of Statistical Natural Language Processing

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